Exploring processed UVP-2017 data

There are binned and unbinned data that I would like to look at.

First the unbinned data. I want for each, number, biovol, flux, psd, psd with confidence intervals. read_csv(“dataOut/”)

library(tidyverse)
library(cowplot)
library(plotly)

Load all data

unbinned_DepthSummary <- read_csv("dataOut/unbinned_DepthSummary.csv")

Parsed with column specification:
cols(
  .default = col_double(),
  profile = col_character(),
  time = col_datetime(format = "")
)
See spec(...) for full column specifications.

unbinned_EachSize <- read_csv("dataOut/unbinned_EachSize.csv")

Parsed with column specification:
cols(
  profile = col_character(),
  time = col_datetime(format = ""),
  depth = col_double(),
  psd_gam = col_double(),
  vol = col_double(),
  sizeclass = col_character(),
  lb = col_double(),
  ub = col_double(),
  binsize = col_double(),
  TotalParticles = col_double(),
  nparticles = col_double(),
  n_nparticles = col_double(),
  biovolume = col_double(),
  speed = col_double(),
  flux = col_double(),
  flux_fit = col_double(),
  GamPredictTP = col_double()
)

binned_DepthSummary <- read_csv("dataOut/binned_DepthSummary.csv")

Parsed with column specification:
cols(
  .default = col_double(),
  profile = col_character(),
  time = col_datetime(format = "")
)
See spec(...) for full column specifications.

binned_EachSize <- read_csv("dataOut/binned_EachSize.csv")

Parsed with column specification:
cols(
  profile = col_character(),
  time = col_datetime(format = ""),
  depth = col_double(),
  psd_gam = col_double(),
  lb = col_double(),
  ub = col_double(),
  binsize = col_double(),
  vol = col_double(),
  TotalParticles = col_double(),
  nparticles = col_double(),
  n_nparticles = col_double(),
  biovolume = col_double(),
  speed = col_double(),
  flux = col_double(),
  flux_fit = col_double(),
  GamPredictTP = col_double()
)

Summary

Binned

plot_nparticles <- unbinned_DepthSummary %>% ggplot(aes(x = tot_nparticles, y = depth, colour = profile)) + geom_point(alpha = 0.5, size = 1) + scale_y_reverse() + scale_x_log10() + guides(colour = FALSE) + labs(x = "#Particles/L") + theme_cowplot()

plot_biovolume <- unbinned_DepthSummary %>% ggplot(aes(x = tot_biovolume, y = depth, colour = profile)) + geom_point(alpha = 0.5, size = 1) + scale_y_reverse() + scale_x_log10() + guides(colour = FALSE) + labs(x = "Unadjusted Biovolume (mass units/L)")+ theme_cowplot()

plot_flux <- unbinned_DepthSummary %>% ggplot(aes(x = tot_flux, y = depth, colour = profile)) + geom_point(alpha = 0.5, size = 1) + scale_y_reverse() + scale_x_log10() + guides(colour = FALSE) + labs(x = "Unadjusted Flux (units/m^2/day)")+
   theme_cowplot()

plot_psd <- unbinned_DepthSummary %>% ggplot(aes(x = psd, y = depth, colour = profile)) + geom_point(alpha = 0.5, size = 1, shape = 21, fill = "black", stroke = 1) + scale_y_reverse()  + guides(colour = FALSE) + labs(x = "Particle Size Distribution Slope") + theme_cowplot()

plot_speed <- unbinned_DepthSummary %>% ggplot(aes(x = tot_speed, y = depth, colour = profile)) + geom_point(alpha = 0.5, size = 1) + scale_y_reverse() + scale_x_log10() + guides(colour = FALSE) + labs(x = "Unadjusted Flux Weighted Sinking Speed (m/d)") + theme_cowplot()


ggplotly(plot_nparticles)

ggplotly(plot_biovolume)

ggplotly(plot_flux)

ggplotly(plot_speed)

ggplotly(plot_psd)

cowplot::plot_grid(plot_nparticles,plot_biovolume, plot_flux, plot_speed, plot_psd) #%>% ggplotly()

plot_nparticles

plot_psd + scale_x_continuous(limits = c(-4.6, -2.5))

Binned

plot_nparticles <- binned_DepthSummary %>% ggplot(aes(x = tot_nparticles, y = depth, colour = profile)) + geom_point(alpha = 0.5, size = 1) + scale_y_reverse(breaks = seq(from = 0, to = 2500, by = 100)) + scale_x_log10() + guides(colour = FALSE) + labs(x = "#Particles/L") + theme_cowplot()

plot_biovolume <- binned_DepthSummary %>% ggplot(aes(x = tot_biovolume, y = depth, colour = profile)) + geom_point(alpha = 0.5, size = 1) + scale_y_reverse() + scale_x_log10() + guides(colour = FALSE) + labs(x = "Unadjusted Biovolume (mass units/L)")+ theme_cowplot()

plot_flux <- binned_DepthSummary %>% ggplot(aes(x = tot_flux, y = depth, colour = profile)) + geom_point(alpha = 0.5, size = 1) + scale_y_reverse() + scale_x_log10() + guides(colour = FALSE) + labs(x = "Unadjusted Flux (units/m^2/day)")+
   theme_cowplot()

plot_psd <- binned_DepthSummary %>% ggplot(aes(x = psd, y = depth, colour = profile)) + geom_point(alpha = 0.5, size = 1, shape = 21, fill = "black") + scale_y_reverse()  + guides(colour = FALSE) + labs(x = "Particle Size Distribution Slope") + theme_cowplot()

plot_speed <- binned_DepthSummary %>% ggplot(aes(x = tot_speed, y = depth, colour = profile)) + geom_point(alpha = 0.5, size = 1) + scale_y_reverse() + scale_x_log10() + guides(colour = FALSE) + labs(x = "Unadjusted Flux Weighted Sinking Speed (m/d)") + theme_cowplot()


ggplotly(plot_nparticles)

ggplotly(plot_biovolume)

ggplotly(plot_flux)

ggplotly(plot_speed)

ggplotly(plot_psd)

cowplot::plot_grid(plot_nparticles,plot_biovolume, plot_flux, plot_speed, plot_psd) #%>% ggplotly()